Hailiang Chen

About Me

My name is Hailiang Chen. I'm currently an Associate Professor at Department of Information Systems, City University of Hong Kong.
My research interests include social media, multi-channel management, FinTech, big data, and business analytics.

I received a doctoral degree in Management Information Systems and a master degree in Economics from Purdue University and a bachelor degree in Information Management and Information Systems from Tsinghua University.

Latest Projects

The Emergence of "Social Executives" and Its Consequences for Financial Markets

Authors: Hailiang Chen, Byoung-Hyoun Hwang, and Baixiao Liu

Abstract:
We document the emergence of “social executives,” top executives who connect with investors directly, personally, and in real time through social media, and we study the consequences of this development for financial markets. We contend that the emergence of social executives enables retail investors to obtain value-relevant information to which they previously had no access, and we conjecture that this increases retail investor participation and improves stock market liquidity. Using data reflecting the personal Twitter account activity of the CEOs and CFOs of the largest publicly traded companies in the United States, we find evidence consistent with our hypothesis. Utilizing the Securities and Exchange Commission’s recent embrace of social media as a plausibly exogenous shock, we also provide evidence for a causal link. We conclude that the emergence of social executives has important consequences for financial markets.

The Causal Effect of Video Streaming on DVD Sales: Evidence from a Natural Experiment

Abstract:
Video streaming services recently become a revenue driver of the home entertainment industry. By contrast, revenue from physical media continuously declines. Content owners, such as movie studios, face the important question of whether streaming media cannibalize the sales of physical media and to what extent. We answer these questions by exploiting a natural experiment that occurred on October 1, 2015 when Epix switched its streaming partner from Netflix to Hulu. This event created an exogenous shock that reduced the streaming availability of Epix’s content because of the significant difference in the market shares of the two video streaming sites. This occurrence allowed us to investigate the causal effect of streaming services on physical DVD sales. Our difference-in-difference analyses show that the decline in the streaming availability of Epix’s content causes a 24.7% increase in their DVD sales in the three months after the event. Our results validate the industry’s concern that video streaming services displace physical DVD sales. In addition, we find that cannibalization between the two media is stronger for DVDs released more recently and for movies with better box office performances. This study contributes to the understanding of the competition between streaming media and physical media and provides important managerial implications for content owners in selecting appropriate movies for streaming.

Monetary Incentive and Stock Opinions on Social Media

Authors: Hailiang Chen, Yu Jeffrey Hu, and Shan Huang

Abstract:
Social media not only is a new channel to obtain financial market information but also becomes the venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to both existing and new amateur analysts on social media and its implications for online investor communities. We find that monetary incentive is effective in increasing the amount of content output but leads to neither better nor worse stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of the role of monetary incentive in stimulating the sharing of value-relevant information by investors in social media communities.

Interplay between Traditional Media and Social Media: Moderating Role of Product Appeal

Abstract:
Multiple media have been widely employed to promote brands, but previous literature documents different and opposing results in terms of how media interaction affects business outcomes and operations. This paper extends the multichannel management literature in operations management by examining the interplay between traditional media and social media channels and how product appeal moderates this interaction in the motion picture industry. Our results provide important implications for movie distributors’ operations decisions (e.g., distribution planning): We first develop a stylized analytical model to show that traditional media channel is more likely to complement social media channel for narrow appeal products, but is more likely to substitute social media channel for broad appeal products. Then, we empirically test this analytical result using social media data collected from Facebook and traditional advertising expenditure data from Kantar Media. The empirical results lend support to our central hypothesis. This study provides a theoretical explanation that reconciles the mixed results in the multichannel management literature and offers managerial implications for channel coordination decisions.

Network Structure and Predictive Power of Social Media for the Bitcoin Market

Authors: Peng Xie, Hailiang Chen, and Yu Jeffrey Hu

Abstract:
Following the recent discovery of social media’s predictive power for financial markets, we try to advance the literature by evaluating the role of social media network structure in distinguishing between value-relevant information and noises. Using data from the Bitcoin market, we provide empirical evidence that loosely-connected social media discussion networks are more accurate in predicting future returns. Although social media information linkages cause information free riding and damage the overall network prediction accuracy, they nevertheless serve as landmarks for identifying informed social media participants: value-relevant information is more likely to be shared by authors who stimulate active discussions among their peers. We also document a positive relationship between network connectedness and future trading intensity. Our study highlights the importance of leveraging network structures to improve the prediction accuracy of social media analytics for financial markets.

Fake News, Investor Attention, and Market Reaction

Authors: Jonathan Clarke, Hailiang Chen, Ding Du, and Yu Jeffrey Hu

Abstract:
Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? We use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, we find that fake news stories generate significantly more attention than a control sample of legitimate articles. We find no evidence that article commenters can detect fake news. Seeking Alpha editors have only modest ability to detect fake news. The broader stock market appears to price fake news correctly. The stock price reaction to the release of fake news is not significantly different than a matched control sample over short and longer-term windows. We conclude by presenting a machine learning algorithm that is successful in identifying fake news articles.